Final Project

Reproducible data retrieval

options("icpsr_email" = "dy212@georgetown.edu", "icpsr_password" = "Fbp9nbmKreLsubd8nL5H")

crime

# takes 1-2 mins
icpsr_download(
  file_id = 38649,
  download_dir = here("data")
)

crime<-read_dta(here("data","ICPSR_38649","DS0001","38649-0001-Data.dta"))

Clean data

crime

# crime data at 2013
crime_data<-crime|>
  rename_all(tolower)|>
  rename(county=stcofips)|>
  mutate(viol = murder+rape+robbery+agasslt)|>
  mutate(property = burglry+larceny+mvtheft)|>
  select(county,year,viol,property)

attr(crime_data$viol, "label")<-"Total violent crimes reported (MURDER + RAPE + ROBBERY + AGASSLT)"

attr(crime_data$property, "label")<-"Total property crimes reported (BURGLRY + LARCENY + MVTHEFT)"

# Extract state-level FIPS codes (first two digits of county FIPS codes)

crime_data$state_fips <- 
  substr(crime_data$county, 
         1, 2)

state_fips_to_abb <- 
  tibble(
  state_fips = c("01", "02", "04", "05", "06", 
                 "08", "09", "10", "11", "12", 
                 "13", "15", "16", "17", "18", 
                 "19", "20", "21", "22", "23", 
                 "24", "25", "26", "27", "28", 
                 "29", "30", "31", "32", "33", 
                 "34", "35", "36", "37", "38", 
                 "39", "40", "41", "42", "44", 
                 "45", "46", "47", "48", "49", 
                 "50", "51", "53", "54", "55", "56"),
  state_abb = c("AL", "AK", "AZ", "AR", "CA", "CO", 
                "CT", "DE", "DC", "FL", "GA", "HI", 
                "ID", "IL", "IN", "IA", "KS", "KY", 
                "LA", "ME", "MD", "MA", "MI", "MN", 
                "MS", "MO", "MT", "NE", "NV", "NH", 
                "NJ", "NM", "NY", "NC", "ND", "OH", 
                "OK", "OR", "PA", "RI", "SC", "SD", 
                "TN", "TX", "UT", "VT", "VA", "WA", 
                "WV", "WI", "WY")
)

Map

# Convert county-level data to state-level data, aggregate violent crime data

crime_data_map <- crime_data |>
  group_by(state_fips) |>
  summarise(
    total_violence = sum(viol, 
                         na.rm = TRUE),
    total_property = sum(property, 
                         na.rm = TRUE)  
  ) |>
  
  left_join(state_fips_to_abb, 
            by = 'state_fips') |>
  
  mutate(
    hover_violence = paste(state_abb, 
                           '<br>', 
                           "Total Violent Crimes: ", 
                           total_violence),
    hover_property = paste(state_abb, 
                           '<br>', 
                           "Total Property Crimes: ", 
                           total_property)  
  ) |>
  
  pivot_longer(
    cols = c(total_violence, 
             total_property),
    names_to = "crime_type",
    values_to = "crime_count"
  )

# Create and output the map for total violence

violence_map <- 
  plot_geo(crime_data_map |>
             filter(crime_type == "total_violence"),
           locationmode = 'USA-states') |>
  
  add_trace(
    z = ~crime_count, 
    text = ~hover_violence,  
    hoverinfo = 'text',     
    locations = ~state_abb,
    color = ~crime_count,
    colors = c("#1a9641", "#ffffbf", "#fdae61", "#d7191c"),
    colorbar = list(title = "Total Violent Crimes (million)")
  ) |>
  
  layout(
    title = 'US Crimes by State, 2009 - 2014',
    geo = list(
      scope = 'usa',
      projection = list(type = 'albers usa'),
      showlakes = TRUE,
      lakecolor = toRGB('white')
    )
  )

# Create and output the map for total property

property_map <- 
  plot_geo(crime_data_map |>
             filter(crime_type == "total_property"),
           locationmode = 'USA-states') |>
  
  add_trace(
    z = ~crime_count, 
    text = ~hover_property,   
    hoverinfo = 'text',       
    locations = ~state_abb,
    color = ~crime_count,
    colors = c("#2b83ba", "#ffffbf", "#fdae61", "#d7191c"),
    colorbar = list(title = "Total Property Crimes (million)")
  ) |>
  
  layout(
    title = 'US Crimes by State, 2009 - 2014',
    geo = list(
      scope = 'usa',
      projection = list(type = 'albers usa'),
      showlakes = TRUE,
      lakecolor = toRGB('white')
    )
  )

# Place the two maps side by side in a stacked layout
fig <- subplot(violence_map, 
               property_map, 
               nrows = 2, 
               margin = 0.05) 

# Print the figure
fig